·
concept
in research methods
In scientific research, concepts are the
abstract ideas or phenomena that are being studied (e.g., educational
achievement). Variables are properties or characteristics of the concept (e.g.,
performance at school), while indicators are ways of measuring or quantifying
variables (e.g., yearly grade reports).
Concepts are based on our experiences.
Concepts can be based on real phenomena and are a generalized idea of something
of meaning. Examples of concepts include common demographic measures: Income,
Age, Eduction Level, Number of SIblings.
Constructs
are broad concepts or topics for a study. Constructs
can be conceptually defined in that they have meaning in theoretical terms.
They can be abstract and do not necessarily need to be directly observable.
Examples of constructs include intelligence or life satisfaction.
A construct is an image or abstract idea
specifically invented for a given research and/or theory-building purpose.
The Role of Constructs
A construct is an abstract idea inferred
from specific instances that are thought to be related. } Typical
marketing constructs are brand loyalty, satisfaction, preference, awareness,
knowledge. } Research objectives typically call for the measurement of
constructs. } There are customary methods for defining and measuring constructs.
These are broad concepts for study-
abstract / not directly visible / may be complex
Examples : Agression, love, intelligence,
satisfaction,
Conceptualization
Definition: the process through which we
specify what we will mean when we use particular terms in research.
Conceptualization produces specific,
agreed-upon meaning for a concept for the purposes of research.
Process of specifying clearly exactly
what you mean by a term
This process of specifying exact meaning
involves describing the indicators we’ll be using to measure our concept and
the different aspects of the concept, called dimensions.
Operationalization
Operational
definition: specifies precisely how a concept will be measured – the operations
it will perform. } process whereby researchers specify
empirical concepts that can be taken as indicators of the attributes of a
concept.
Variables
· What are variables?
·
Variables are things you
measure, manipulate and control in statistics and research. All studies analyze
a variable, which can describe a person, place, thing or idea.
·
In research, variables
are any characteristics that can take on different values, such as
height, age, temperature, or test scores. Researchers often manipulate or
measure independent and dependent variables in studies to test cause-and-effect
relationships.
A variable's value can change between
groups or over time. For example, if the variable in an experiment is a
person's eye color, its value can change from brown to blue to green from
person to person.
Types of variables
Researchers organize variables into a
variety of categories, the most common of which include:
What are the two
main types of variables?
-
Independent
and dependent variables in terms of
cause and effect: an independent variable is the variable you think is the
cause, while a dependent variable is the effect. In an experiment, you
manipulate the independent variable and measure the outcome in the dependent
variable.
-
An experiment usually has three
kinds of variables: independent, dependent, and controlled.
10 Types of Variables in Research and
Statistics
1. Independent variables
An independent variable is a singular
characteristic that the other variables in your experiment cannot change. Age
is an example of an independent variable. Where someone lives, what they eat or
how much they exercise are not going to change their age. Independent variables
can, however, change other variables. In studies, researchers often try to find
out whether an independent variable causes other variables to change and in
what way.
2. Dependent variables
A dependent variable relies on and can be
changed by other components. A grade on an exam is an example of a dependent
variable because it depends on factors such as how much sleep you got and how
long you studied. Independent variables can influence dependent variables, but
dependent variables cannot influence independent variables. For example, the
time you spent studying (dependent) can affect the grade on your test
(independent) but the grade on your test does not affect the time you spent
studying.
When analyzing relationships between study
objects, researchers often try to determine what makes the dependent variable
change and how.
3. Intervening variables
An intervening variable, sometimes called a
mediator variable, is a theoretical variable the researcher uses to explain a
cause or connection between other study variables—usually dependent and
independent ones. They are associations instead of observations. For example,
if wealth is the independent variable, and a long life span is a dependent
variable, the researcher might hypothesize that access to quality healthcare is
the intervening variable that links wealth and life span.
4. Moderating variables
A moderating or moderator variable changes
the relationship between dependent and independent variables by strengthening
or weakening the intervening variable's effect. For example, in a study looking
at the relationship between economic status (independent variable) and how
frequently people get physical exams from a doctor (dependent variable), age is
a moderating variable. That relationship might be weaker in younger individuals
and stronger in older individuals.
5. Control variables
Control or controlling variables are
characteristics that are constant and do not change during a study. They have
no effect on other variables. Researchers might intentionally keep a control
variable the same throughout an experiment to prevent bias. For example, in an
experiment about plant development, control variables might include the amounts
of fertilizer and water each plant gets. These amounts are always the same so
that they do not affect the plants' growth.
6. Extraneous variables
Extraneous variables are factors that
affect the dependent variable but that the researcher did not originally
consider when designing the experiment. These unwanted variables can
unintentionally change a study's results or how a researcher interprets those
results. Take, for example, a study assessing whether private tutoring or
online courses are more effective at improving students' Spanish test scores.
Extraneous variables that might unintentionally influence the outcome include
parental support, prior knowledge of a foreign language or socioeconomic
status.
7. Quantitative variables
Quantitative variables are any data sets
that involve numbers or amounts. Examples might include height, distance or
number of items. Researchers can further categorize quantitative variables into
two types:
- Discrete: Any numerical
variables you can realistically count, such as the coins in your wallet or
the money in your savings account.
- Continuous: Numerical
variables that you could never finish counting, such as time.
8. Qualitative variables
Qualitative, or categorical, variables are
non-numerical values or groupings. Examples might include eye or hair color.
Researchers can further categorize qualitative variables into three types:
- Binary: Variables with only
two categories, such as male or female, red or blue.
- Nominal: Variables you can
organize in more than two categories that do not follow a particular
order. Take, for example, housing types: Single-family home, condominium,
tiny home.
- Ordinal: Variables you can
organize in more than two categories that follow a particular order. Take,
for example, level of satisfaction: Unsatisfied, neutral, satisfied.
9. Confounding variables
A confounding variable is one you did not
account for that can disguise another variable's effects. Confounding variables
can invalidate your experiment results by making them biased or suggesting a
relationship between variables exists when it does not. For example, if you are
studying the relationship between exercise level (independent variable) and body
mass index (dependent variable) but do not consider age's effect on these
factors, it becomes a confounding variable that changes your results.
10. Composite variables
A composite variable is two or more
variables combined to make a more complex variable. Overall health is an
example of a composite variable if you use other variables, such as weight,
blood pressure and chronic pain, to determine overall health in your experiment.
Attribute
Attribute is a quality, character, or
characteristic ascribed to someone or something
Attributes refer to the
characteristics of the item under study, like the habit of smoking, or
drinking. So 'smoking' and 'drinking' both refer to the example of an
attribute.
In science and research, an attribute
is a quality of an object (person, thing, etc.). Attributes are closely related to
variables. A variable is a logical set of
attributes. Variables can "vary" – for example, be high or low.
In statistical studies, variables
are the quantifiable values or sets that vary over time. Attributes
are the characteristic of a thing related to quality that is not quantifiable.
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https://theintactone.com/2019/02/19/rm-u1-topic-5-conceptions-construct-attribute-variables-hypotheses/ |
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A hypothesis states your predictions about what your research will find. It is
a tentative answer to your research question that has not yet been tested.
For some research projects, you might have to write several hypotheses that
address different aspects of your research question. A hypothesis is not just a guess — it should be
based on existing theories and knowledge. It also has to be testable, which
means you can support or refute it through scientific research methods (such
as experiments, observations and statistical analysis of data). |
When research problem is clear…. } And at
least broad research questions are formulated…. the next step is to }
Determine the Relevant Variables to the Situation } In this
step, the researcher and decision maker jointly determine the specific
variables pertinent to each defined problem or question that needs to be
answered. The focus is on identifying the different independent and dependent
variables. Determination must be made as to the types of information (i.e.,
facts, estimates, predictions, relationships) and specific constructs that are
relevant to the decision problem. } Construct =
concepts or ideas about an object, attribute, or phenomenon that are worthy of
measurement
In other words…The next step after RQ
formulation can be also…. } Choice and formulation of concepts and constructs impotant for the
problem } Formulation of hypotheses } Formulation of
variables } …..formulation of constructs, hypotheses and variables is usually
not sequentional process, but the steps that are done more or less simultaneously
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What are the units of
analysis in research? Simply put, the unit of
analysis is basically the 'who' or what' that the researcher is
interested in analyzing. For instance, an individual a group,
organization, country, social phenomenon, etc. A unit of observation is any
item from which data can be collected and measured. The unit of analysis is the entity
that frames what is being looked at in a study, or is the entity being
studied as a whole.[1] In social
science research, at the macro level, the most commonly referenced
unit of analysis, considered to be a society is the state
(polity) (i.e. country). At meso level, common units of observation
include groups, organizations, and institutions, and at micro level,
individual people. For example, if your research is based around data
on exam grades for students at two different universities, then the unit of
analysis is the data for the individual student due to each student having an
exam score associated with them. What are the five units of analysis? In sociology, the most common units of analysis are
individuals, groups, social interactions, organizations and institutions, and
social and cultural artifacts. Unit of Analysis One of the most important ideas in a research
project is the unit of analysis. The unit of analysis is the
major entity that you are analyzing in your study. For instance, any of the
following could be a unit of analysis in a study:
Why is it called the ‘unit of analysis’ and not
something else (like, the unit of sampling)? Because it is the
analysis you do in your study that determines what the unit is. For
instance, if you are comparing the children in two classrooms on achievement
test scores, the unit is the individual child because you have a score for
each child. On the other hand, if you are comparing the two classes on
classroom climate, your unit of analysis is the group, in this case the
classroom, because you only have a classroom climate score for the class as a
whole and not for each individual student. For different analyses in the same
study you may have different units of analysis. If you decide to base an
analysis on student scores, the individual is the unit. But you might decide
to compare average classroom performance. In this case, since the data that
goes into the analysis is the average itself (and not the individuals’
scores) the unit of analysis is actually the group. Even though you had data
at the student level, you use aggregates in the analysis. In many areas of
social research these hierarchies of analysis units have become particularly
important and have spawned a whole area of statistical analysis sometimes
referred to as hierarchical modeling. This is true in education,
for instance, where we often compare classroom performance but collected
achievement data at the individual student level. |
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